{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34qVD_ntSKLF"
   },
   "source": [
    "# Learning to optimize parametric nonlinear programming problem (pNLP) using differentiable projected gradient method in Neuromancer.\n",
    "\n",
    "\n",
    "This is an interactive notebook based on the python script [Part_4_projectedGradient.py](./Part_4_projectedGradient.py).  \n",
    "\n",
    "We demonstrate how the NeuroMANCER toolbox can be used to solve parametric constrained [Rosenbrock problem](https://en.wikipedia.org/wiki/Rosenbrock_function):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
    "& && x \\ge y\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "with parameters $p, a$ and decision variables $x, y$.\n",
    "\n",
    "References:  \n",
    "    [projected gradient method](https://neos-guide.org/guide/algorithms/gradient-projection/)  \n",
    "    [Deep Constraint Completion and Correction (DC3) paper](https://arxiv.org/abs/2104.12225)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCn3zpaIqgMc"
   },
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qzy5Wot5k2Gf"
   },
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "X_3EvkSz0Fnz",
    "outputId": "23c06f6b-ab48-4763-c43c-40a325cacf87",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:48.039486Z",
     "start_time": "2025-07-07T18:11:48.023827Z"
    }
   },
   "source": "!pip install neuromancer",
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LWyvndXlz0Fv"
   },
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "KbP0n-4evRqt",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:57.917445Z",
     "start_time": "2025-07-07T18:11:48.039486Z"
    }
   },
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import neuromancer.slim as slim\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as patheffects\n",
    "from casadi import *\n",
    "import casadi\n",
    "import time"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\drgon\\anaconda3\\envs\\neuromancer-dev\\lib\\site-packages\\mlflow\\utils\\requirements_utils.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
      "  import pkg_resources\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "POL27EJZxJmI",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.060167Z",
     "start_time": "2025-07-07T18:11:58.044084Z"
    }
   },
   "source": [
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.modules import blocks, solvers\n",
    "from neuromancer.system import Node"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WH7o7Wu1epw"
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_r6p2p6myHAh",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.092236Z",
     "start_time": "2025-07-07T18:11:58.076121Z"
    }
   },
   "source": [
    "data_seed = 408  # random seed used for simulated data\n",
    "np.random.seed(data_seed)\n",
    "torch.manual_seed(data_seed);"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JZ9qrw0tlJhs"
   },
   "source": [
    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Nu58M-8JyHy6",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.126982Z",
     "start_time": "2025-07-07T18:11:58.110935Z"
    }
   },
   "source": [
    "nsim = 5000  # number of datapoints: increase sample density for more robust results\n",
    "# create dictionaries with sampled datapoints with uniform distribution\n",
    "a_low, a_high, p_low, p_high = 0.2, 1.2, 0.5, 2.0\n",
    "samples_train = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
    "                 \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_dev = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_test = {\"a\": torch.FloatTensor(nsim, 1).uniform_(a_low, a_high),\n",
    "               \"p\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "# create named dictionary datasets\n",
    "train_data = DictDataset(samples_train, name='train')\n",
    "dev_data = DictDataset(samples_dev, name='dev')\n",
    "test_data = DictDataset(samples_test, name='test')\n",
    "# create torch dataloaders for the Trainer\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,\n",
    "                                           collate_fn=train_data.collate_fn, shuffle=True)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=dev_data.collate_fn, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=test_data.collate_fn, shuffle=True)\n",
    "# note: training quality will depend on the DataLoader parameters such as batch size and shuffle"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.321241Z",
     "start_time": "2025-07-07T18:11:58.142887Z"
    }
   },
   "source": [
    "# visualize taining and test samples for 2D parametric space\n",
    "a_train = samples_train['a'].numpy()\n",
    "p_train = samples_train['p'].numpy()\n",
    "a_dev = samples_dev['a'].numpy()\n",
    "p_dev = samples_dev['p'].numpy()\n",
    "plt.figure()\n",
    "plt.scatter(a_train, p_train, s=2., c='blue', marker='o')\n",
    "plt.scatter(a_dev, p_dev, s=2., c='red', marker='o')\n",
    "plt.title('Sampled parametric space for training')\n",
    "plt.xlim(a_low, a_high)\n",
    "plt.ylim(p_low, p_high)\n",
    "plt.grid(True)\n",
    "plt.xlabel('a')\n",
    "plt.ylabel('p')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n",
    "plt.show(block=True)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q6DqbTdL6S4o"
   },
   "source": [
    "# pNLP Formulation in NeuroMANCER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2htUaWMDjsk"
   },
   "source": [
    "## Primal Solution Map Architecture\n",
    "\n",
    "we define symbolic wrapper for a neural network architecture creating a mapping of problem parameters $a, p$ onto decision variables $x$:  \n",
    "$x = net(a, p)$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Ta_I_pjyyLzf",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.368835Z",
     "start_time": "2025-07-07T18:11:58.355041Z"
    }
   },
   "source": [
    "# define neural architecture for the trainable solution map\n",
    "func = blocks.MLP(insize=2, outsize=2,\n",
    "                bias=True,\n",
    "                linear_map=slim.maps['linear'],\n",
    "                nonlin=nn.ReLU,\n",
    "                hsizes=[80] * 4)\n",
    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
    "sol_map = Node(func, ['a', 'p'], ['x'], name='map')"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lxj77EFj7EO-"
   },
   "source": [
    "## Objective and Constraints in NeuroMANCER\n",
    "\n",
    "construct symbolic objectives and constraints of the problem:  \n",
    "$$\n",
    "\\begin{align}\n",
    "&\\text{minimize } &&  (1-x)^2 + a(y-x^2)^2\\\\\n",
    "&\\text{subject to} && \\left(\\frac{p}{2}\\right)^2 \\le x^2 + y^2 \\le p^2\\\\\n",
    "& && x \\ge y\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bcoVjphjyPp9",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.416060Z",
     "start_time": "2025-07-07T18:11:58.400242Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "variable is a basic symbolic abstraction in Neuromancer\n",
    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
    "variable construction supports:\n",
    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
    "to visualize computational graph of the variable use x.show() method          \n",
    "\"\"\"\n",
    "\n",
    "# define primal decision variables\n",
    "x = variable(\"x\")[:, [0]]\n",
    "y = variable(\"x\")[:, [1]]\n",
    "# problem parameters sampled in the dataset\n",
    "p = variable('p')\n",
    "a = variable('a')\n",
    "\n",
    "# objective function\n",
    "f = (1-x)**2 + a*(y-x**2)**2\n",
    "obj = f.minimize(weight=1.0, name='obj')\n",
    "\n",
    "# constraints\n",
    "Q_con = 1.0  # constraint penalty weights set deliberately too low to demonstrate \n",
    "con_1 = Q_con*(x >= y)\n",
    "con_2 = Q_con*((p/2)**2 <= x**2+y**2)\n",
    "con_3 = Q_con*(x**2+y**2 <= p**2)\n",
    "con_1.name = 'c1'\n",
    "con_2.name = 'c2'\n",
    "con_3.name = 'c3'"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differentiable projected gradient layer\n",
    "\n",
    "use unrolled gradient projection algorithm as a layer in the symbolic computational graph correcting the initial solution from the neural network:  \n",
    "$$\n",
    "x = net(a, p)  \\\\\n",
    "x_{\\text{projected}} = GradientProjection(x) \n",
    "$$\n",
    "\n",
    "the $GradientProjection$ layer implements a simple iterative algorithm for constraints correction:  \n",
    "$$\n",
    "x_{projected} = x - \\alpha \\nabla \\ell(g(x))\n",
    "$$  \n",
    "where $g(x)$ represents a left hand side of the inequality constraints in the standard form:  \n",
    "$$g(x) \\le 0$$  \n",
    "$\\ell(\\cdot)$ defines the constraints violation penalty, for instance given as:  \n",
    "$$\n",
    "\\ell(\\cdot) = || \\text{ReLU}(\\cdot)  ||_2^2\n",
    "$$  \n",
    "and $\\alpha$ gives the step size. For better convergence the method can be used over multiple steps $k$ with varying stepsize $\\alpha_k$, e.g. by using momentum.  \n",
    "\n",
    "For further details see the [Neuromancer implementation](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/modules/solvers.py#L45) or the [Deep Constraint Completion and Correction (DC3) paper](https://arxiv.org/abs/2104.12225)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.431793Z",
     "start_time": "2025-07-07T18:11:58.422085Z"
    }
   },
   "source": [
    "# instantiate projected gradient layer to correct the solutions from the neural net:\n",
    "# proj(sol_map(xi)) -> x\n",
    "num_steps = 5\n",
    "step_size = 0.1\n",
    "proj = solvers.GradientProjection(constraints=[con_2, con_3],          # inequality constraints to be corrected\n",
    "                                  input_keys=[\"x\"],                    # primal variables to be updated\n",
    "                                  num_steps=num_steps,                 # number of rollout steps of the solver method\n",
    "                                  step_size=step_size,                 # step size of the solver method\n",
    "                                  decay=0.1,                           # decay factor of the step size\n",
    "                                  name='proj')"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct differentiable optimization problem\n",
    "\n",
    "Putting things together in a single differentiable symbolic computational graph constructed via Neuromancer's [Problem class](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/problem.py#L18) using [differentiable loss functions](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/loss.py)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "n7VPa9Wc8JRB",
    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.479741Z",
     "start_time": "2025-07-07T18:11:58.463619Z"
    }
   },
   "source": [
    "# constrained optimization problem construction\n",
    "objectives = [obj]\n",
    "constraints = [con_1, con_2, con_3]\n",
    "components = [sol_map, proj]\n",
    "    \n",
    "# create penalty method loss function\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss,\n",
    "                  grad_inference=True   # argument for allowing computation of gradients at the inference time\n",
    "                  )\n",
    "# problem.show()"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icWSMeG28SKc"
   },
   "source": [
    "## Parametric Problem Solution in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "rk1bRczByUvl",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.526428Z",
     "start_time": "2025-07-07T18:11:58.510679Z"
    }
   },
   "source": [
    "lr = 0.001      # step size for gradient descent\n",
    "epochs = 50    # number of training epochs\n",
    "warmup = epochs    # number of epochs to wait before enacting early stopping policy\n",
    "patience = epochs  # number of epochs with no improvement in eval metric to allow before early stopping"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "x4oS2N2ZyWtD",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:11:58.574060Z",
     "start_time": "2025-07-07T18:11:58.558299Z"
    }
   },
   "source": [
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
    "\n",
    "# define trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_loader,\n",
    "    optimizer,\n",
    "    epochs=epochs,\n",
    "    patience=patience,\n",
    "    warmup=warmup)"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27ASQD3q-M0A",
    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.181032Z",
     "start_time": "2025-07-07T18:11:58.605961Z"
    }
   },
   "source": [
    "# Train NLP solution map\n",
    "best_model = trainer.train()\n",
    "best_outputs = trainer.test(best_model)\n",
    "# load best model dict\n",
    "problem.load_state_dict(best_model)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 0.13619667291641235\n",
      "epoch: 1  train_loss: 0.05767969414591789\n",
      "epoch: 2  train_loss: 0.052668966352939606\n",
      "epoch: 3  train_loss: 0.05183699354529381\n",
      "epoch: 4  train_loss: 0.051352038979530334\n",
      "epoch: 5  train_loss: 0.05091703310608864\n",
      "Interrupted training loop.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0hhUw4PVBWmb"
   },
   "source": [
    "## Get pNLP solution from trained neural network"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.218866Z",
     "start_time": "2025-07-07T18:12:19.212858Z"
    }
   },
   "source": [
    "# selected problem parameters\n",
    "p = 1.0\n",
    "a = 1.0"
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "os3I3I8L3HbE",
    "outputId": "50c13f99-7693-4102-b65c-4f3707f90c29",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.265480Z",
     "start_time": "2025-07-07T18:12:19.249757Z"
    }
   },
   "source": [
    "# Solution to mpNLP via Neuromancer\n",
    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
    "             'name': 'test'}\n",
    "model_out = problem(datapoint)\n",
    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
    "print(x_nm)\n",
    "print(y_nm)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.80091256\n",
      "0.5662299\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-KmqqUj-Q3E"
   },
   "source": [
    "## Get pNLP solution from CasADi for comparison\n",
    "\n",
    "[CasADi](https://web.casadi.org/) is an open-source tool for constrained optimization and optimal control that has influenced the development of NeuroMANCER."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "dmJERFP2yYuC",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.334363Z",
     "start_time": "2025-07-07T18:12:19.302826Z"
    }
   },
   "source": [
    "# instantiate casadi optimizaiton problem class\n",
    "def NLP_param(a, p, opti_silent=False):\n",
    "    opti = casadi.Opti()\n",
    "    # define variables\n",
    "    x = opti.variable()\n",
    "    y = opti.variable()\n",
    "    p_opti = opti.parameter()\n",
    "    a_opti = opti.parameter()\n",
    "    # define objective and constraints\n",
    "    opti.minimize((1 - x) ** 2 + a_opti * (y - x ** 2) ** 2)\n",
    "    opti.subject_to(x >= y)\n",
    "    opti.subject_to((p_opti / 2) ** 2 <= x ** 2 + y ** 2)\n",
    "    opti.subject_to(x ** 2 + y ** 2 <= p_opti ** 2)\n",
    "    # select IPOPT solver and solve the NLP\n",
    "    if opti_silent:\n",
    "        opts = {'ipopt.print_level': 0, 'print_time': 0, 'ipopt.sb': 'yes'}\n",
    "    else:\n",
    "        opts = {}\n",
    "    opti.solver('ipopt', opts)\n",
    "    # set parametric values\n",
    "    opti.set_value(p_opti, p)\n",
    "    opti.set_value(a_opti, a)\n",
    "    return opti, x, y\n",
    "\n",
    "# construct casadi problem\n",
    "opti, x, y = NLP_param(a, p)\n",
    "# solve NLP via casadi\n",
    "sol = opti.solve()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.14.11, running with linear solver MUMPS 5.4.1.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        6\n",
      "Number of nonzeros in Lagrangian Hessian.............:        3\n",
      "\n",
      "Total number of variables............................:        2\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:        0\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        3\n",
      "        inequality constraints with only lower bounds:        1\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        2\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  1.0000000e+00 2.50e-01 1.67e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  9.5811923e-01 2.49e-01 1.71e+01  -1.0 5.59e-01    -  5.26e-01 3.81e-02h  1\n",
      "   2  5.1823940e-01 1.40e-02 1.95e+01  -1.0 2.12e+01    -  1.25e-03 3.12e-02f  6\n",
      "   3  3.2701557e-01 0.00e+00 5.64e-01  -1.0 2.47e-01    -  1.00e+00 1.00e+00f  1\n",
      "   4  1.5594126e-01 0.00e+00 9.02e-01  -1.7 4.58e-01    -  3.11e-01 1.00e+00f  1\n",
      "   5  5.3473898e-02 0.00e+00 2.99e-01  -1.7 5.51e-01    -  1.00e+00 1.00e+00h  1\n",
      "   6  5.7915701e-02 0.00e+00 6.95e-03  -1.7 2.57e-02    -  1.00e+00 1.00e+00h  1\n",
      "   7  4.4884709e-02 0.00e+00 7.47e-03  -3.8 9.13e-02    -  8.77e-01 1.00e+00h  1\n",
      "   8  4.1207939e-02 0.00e+00 2.60e-04  -3.8 3.58e-02    -  1.00e+00 1.00e+00h  1\n",
      "   9  4.0921509e-02 0.00e+00 8.22e-07  -5.7 2.98e-03    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  4.0919039e-02 0.00e+00 7.11e-11  -8.6 2.53e-05    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 10\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.0919038633377619e-02    4.0919038633377619e-02\n",
      "Dual infeasibility......:   7.1118860800467587e-11    7.1118860800467587e-11\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.6584596762655841e-09    2.6584596762655841e-09\n",
      "Overall NLP error.......:   2.6584596762655841e-09    2.6584596762655841e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 18\n",
      "Number of objective gradient evaluations             = 11\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 18\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 11\n",
      "Number of Lagrangian Hessian evaluations             = 10\n",
      "Total seconds in IPOPT                               = 0.005\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
      "       nlp_f  |        0 (       0)   8.00us (444.44ns)        18\n",
      "       nlp_g  |        0 (       0)  18.00us (  1.00us)        18\n",
      "  nlp_grad_f  |        0 (       0)  15.00us (  1.25us)        12\n",
      "  nlp_hess_l  |        0 (       0)  16.00us (  1.60us)        10\n",
      "   nlp_jac_g  |        0 (       0)  18.00us (  1.50us)        12\n",
      "       total  |   5.00ms (  5.00ms)   5.07ms (  5.07ms)         1\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MvXjYHNjISrC",
    "outputId": "69a77f30-0ba5-411e-9d15-3cb7f15d9154",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.397864Z",
     "start_time": "2025-07-07T18:12:19.381876Z"
    }
   },
   "source": [
    "print(f\"x = {sol.value(x)}\")\n",
    "print(f\"y = {sol.value(y)}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = 0.8081695826847699\n",
      "y = 0.588949838491767\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pcNq5fOVE4lR"
   },
   "source": [
    "## Compare: NeuroMANCER vs. CasADi\n",
    "\n",
    "When training the neural network, we choose deliberately bad tuning of the constraints penalties to illustrate the correction mechanism of the projected gradient method."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "kvYCfjq6zxxC",
    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a",
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.631676Z",
     "start_time": "2025-07-07T18:12:19.433473Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "Plots\n",
    "\"\"\"\n",
    "x1 = np.arange(-0.5, 1.5, 0.02)\n",
    "y1 = np.arange(-0.5, 1.5, 0.02)\n",
    "xx, yy = np.meshgrid(x1, y1)\n",
    "\n",
    "# eval objective and constraints\n",
    "J = (1 - xx) ** 2 + a * (yy - xx ** 2) ** 2\n",
    "c1 = xx - yy\n",
    "c2 = xx ** 2 + yy ** 2 - (p / 2) ** 2\n",
    "c3 = -(xx ** 2 + yy ** 2) + p ** 2\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "cp = ax.contourf(xx, yy, J,\n",
    "                 levels=[0, 0.05, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0],\n",
    "                 alpha=0.6)\n",
    "fig.colorbar(cp)\n",
    "ax.set_title('Rosenbrock problem')\n",
    "\n",
    "cg1 = ax.contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg1, 'collections') and cg1.collections:\n",
    "    plt.setp(cg1.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "cg2 = ax.contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg2, 'collections') and cg2.collections:\n",
    "    plt.setp(cg2.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "cg3 = ax.contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
    "if hasattr(cg3, 'collections') and cg3.collections:\n",
    "    plt.setp(cg3.collections,\n",
    "            path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "# Solution to pNLP via Neuromancer\n",
    "datapoint = {'a': torch.tensor([[a]]), 'p': torch.tensor([[p]]),\n",
    "             'name': 'test'}\n",
    "model_out = problem(datapoint)\n",
    "x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
    "y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
    "print(x_nm)\n",
    "print(y_nm)\n",
    "\n",
    "# intermediate projection steps\n",
    "x_proj = sol_map(datapoint)\n",
    "proj.num_steps = 1    # set projections steps to 1 for visualisation\n",
    "X_projected = [x_proj['x'].detach().numpy()]\n",
    "for steps in range(num_steps):\n",
    "    proj_inputs = {**datapoint, **x_proj}\n",
    "    x_proj = proj(proj_inputs)\n",
    "    X_projected.append(x_proj['x'].detach().numpy())\n",
    "projected_steps = np.concatenate(X_projected, axis=0)\n",
    "\n",
    "# plot optimal solutions CasADi vs Neuromancer\n",
    "ax.plot(sol.value(x), sol.value(y), 'g*', markersize=20, label='CasADi')\n",
    "ax.plot(x_nm, y_nm, 'r*', fillstyle='none', markersize=20, label='NeuroMANCER')\n",
    "# plot projected steps\n",
    "marker_sizes = list(np.linspace(120, 180, num_steps+1))\n",
    "color_gradient = list(np.linspace(0, 1, num_steps+1))\n",
    "ax.plot(projected_steps[:, 0], projected_steps[:, 1], '--', c='orange',\n",
    "        label='projection steps')\n",
    "ax.scatter(projected_steps[:, 0], projected_steps[:, 1],\n",
    "           s=marker_sizes, marker='*', c=color_gradient, cmap='coolwarm',\n",
    "           label='neural net')\n",
    "plt.legend(bbox_to_anchor=(1.0, 0.15))\n",
    "plt.show(block=True)\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.80091256\n",
      "0.5662299\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:22: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  if hasattr(cg1, 'collections') and cg1.collections:\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:23: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg1.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:27: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  if hasattr(cg2, 'collections') and cg2.collections:\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:28: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg2.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:32: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  if hasattr(cg3, 'collections') and cg3.collections:\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_42540\\2432087878.py:33: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg3.collections,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.693623Z",
     "start_time": "2025-07-07T18:12:19.679544Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.762514Z",
     "start_time": "2025-07-07T18:12:19.747034Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-07T18:12:19.826474Z",
     "start_time": "2025-07-07T18:12:19.810347Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
